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      const root_url = "gaglia-2020-0813";
      const exhibit = {"Groups":[{"Name":"Cell Type","Channels":["e-Cadherin","CD45","Vimentin","aSMA"],"Colors":["ffffff","7ed321","f97aff","007fff"],"Path":"Cell-Type_17__e-Cadherin--1__CD45--19__Vimentin--47__aSMA"},{"Name":"Proliferation","Channels":["e-Cadherin","PCNA","MCM2","Ki67"],"Colors":["ffffff","4a90e2","64c819","ff0000"],"Path":"Proliferation_17__e-Cadherin--13__PCNA--7__MCM2--9__Ki67"},{"Name":"Arrest","Channels":["e-Cadherin","p21","p27"],"Colors":["ffffff","ffff00","ff0000"],"Path":"Arrest_17__e-Cadherin--18__p21--39__p27"},{"Name":"Proliferation and Arrest","Channels":["e-Cadherin","PCNA","MCM2","Ki67","p21","p27"],"Colors":["ffffff","64c819","64c819","64c819","ff0000","ff0000"],"Path":"Proliferation-and-Arrest_17__e-Cadherin--13__PCNA--7__MCM2--9__Ki67--18__p21--39__p27"},{"Name":"Cell Cycle","Channels":["e-Cadherin","CDT1","CCNE1","Geminin","pRB","pHH3"],"Colors":["ffffff","ff0000","ffeb00","7ed321","00b4ff","bd10e0"],"Path":"Cell-Cycle_17__e-Cadherin--23__CDT1--15__CCNE1--27__Geminin--21__pRB--14__pHH3"},{"Name":"DAPI","Channels":["DAPI"],"Colors":["ffffff"],"Path":"DAPI_0__DAPI"},{"Name":"CD45","Channels":["CD45"],"Colors":["7ed321"],"Path":"CD45_1__CD45"},{"Name":"NR2F1","Channels":["NR2F1"],"Colors":["ff7f00"],"Path":"NR2F1_2__NR2F1"},{"Name":"Cyclin B1","Channels":["CCNB1"],"Colors":["50e3c2"],"Path":"Cyclin-B1_6__CCNB1"},{"Name":"MCM2","Channels":["MCM2"],"Colors":["64c819"],"Path":"MCM2_7__MCM2"},{"Name":"Ki67 - Cell Signaling Technology AF488","Channels":["Ki67"],"Colors":["ff0000"],"Path":"Ki67---Cell-Signaling-Technology-AF488_9__Ki67"},{"Name":"Ki67 - Biolegend AF555","Channels":["Ki67"],"Colors":["e2c0ff"],"Path":"Ki67---Biolegend-AF647_10__Ki67"},{"Name":"PCNA","Channels":["PCNA"],"Colors":["4a90e2"],"Path":"PCNA_13__PCNA"},{"Name":"pHH3","Channels":["pHH3"],"Colors":["b8e986"],"Path":"pHH3_14__pHH3"},{"Name":"Cyclin E1","Channels":["CCNE1"],"Colors":["f8e71c"],"Path":"Cyclin-E1_15__CCNE1"},{"Name":"e-Cadherin","Channels":["e-Cadherin"],"Colors":["ffffff"],"Path":"e-Cadherin_17__e-Cadherin"},{"Name":"p21","Channels":["p21"],"Colors":["ffff00"],"Path":"p21_18__p21"},{"Name":"Vimentin","Channels":["Vimentin"],"Colors":["f97aff"],"Path":"Vimentin_19__Vimentin"},{"Name":"pRB","Channels":["pRB"],"Colors":["b995ff"],"Path":"pRB_21__pRB"},{"Name":"Cyclin D1","Channels":["CCND1"],"Colors":["f5a623"],"Path":"Cyclin-D1_22__CCND1"},{"Name":"CDT1","Channels":["CDT1"],"Colors":["ff0000"],"Path":"CDT1_23__CDT1"},{"Name":"Cyclin A1-A2","Channels":["CCNA1/2"],"Colors":["f972ff"],"Path":"Cyclin-A1-A2_25__CCNA1---2"},{"Name":"CD44","Channels":["CD44"],"Colors":["7ed321"],"Path":"CD44_26__CD44"},{"Name":"Geminin","Channels":["Geminin"],"Colors":["237de8"],"Path":"Geminin_27__Geminin"},{"Name":"CD24","Channels":["CD24"],"Colors":["ff007f"],"Path":"CD24_29__CD24"},{"Name":"BMI1","Channels":["BMI1"],"Colors":["c98cff"],"Path":"BMI1_31__BMI1"},{"Name":"p53","Channels":["p53"],"Colors":["fff470"],"Path":"p53_33__p53"},{"Name":"AKT","Channels":["AKT"],"Colors":["2de0ff"],"Path":"AKT_34__AKT"},{"Name":"Nanog","Channels":["Nanog"],"Colors":["ffffff"],"Path":"Nanog_35__Nanog"},{"Name":"HES1","Channels":["HES1"],"Colors":["ffff00"],"Path":"HES1_37__HES1"},{"Name":"ALDH1","Channels":["ALDH1"],"Colors":["3996ff"],"Path":"ALDH1_38__ALDH1"},{"Name":"p27","Channels":["p27"],"Colors":["ff0000"],"Path":"p27_39__p27"},{"Name":"Lamin B1","Channels":["Lamin B1"],"Colors":["00ff7f"],"Path":"Lamin-B1_45__Lamin B1"},{"Name":"GPX4","Channels":["GPX4"],"Colors":["f335f8"],"Path":"GPX4_46__GPX4"},{"Name":"aSMA","Channels":["aSMA"],"Colors":["007fff"],"Path":"aSMA_47__aSMA"}],"Masks":[{"Name":"Cell Type - Immune","Path":"masks/imm","Colors":["FFA500"],"Channels":["Immune"]},{"Name":"Cell Type - Epithelial","Path":"masks/epi","Colors":["FFFF33"],"Channels":["Epithelial"]},{"Name":"Cell Type - Stromal","Path":"masks/str","Colors":["9370DB"],"Channels":["Stromal"]},{"Name":"Cell Type - Other","Path":"masks/other","Colors":["A9A9A9"],"Channels":["Other Cell Type"]},{"Name":"MPI = +1","Path":"masks/plus","Colors":["00c206"],"Channels":["MPI+1"]},{"Name":"MPI = 0","Path":"masks/zero","Colors":["529aff"],"Channels":["MPI0"]},{"Name":"MPI = -1","Path":"masks/minus","Colors":["d61a0d"],"Channels":["MPI-1"]}],"Header":"Cell proliferation is a defining feature of malignancy. Our understanding of this fundamental \nprocess is derived from monocultures of cells propagated under synthetic conditions. However, \ntumors are complex mixtures of cell types and populations comprising proliferative, non-proliferative \nand arrested states. Images of single or small sets of protein markers from fixed tissue samples \nonly provide limited and static views into the nature of these complex states.  We designed a \nmultivariate quantitative framework for characterizing cellular proliferation and cell cycle \ndynamics in tissue sections.\n\nThis story contextualizes these new metrics of Multivariate Proliferation Index (MPI) and cell \ncycle coherence in an untreated human HER2 positive primary breast cancer resection.\n\nFirst, we use established lineage markers (CD45, Vimentin, e-Cadherin and aSMA) to distinguish \nepithelial cells from immune and stromal cells (Waypoint 3. Cell Typing).\n\nThen we use proliferation and cell cycle arrest markers to define a Multivariate Proliferation \nIndex, or MPI (Waypoint 4-7).\n\nFinally we focus on the proliferative population (MPI+1) and extract cell cycle protein dynamics \nfrom tissue images (Waypoint 8-9).\n","PixelsPerMicron":3.0769,"Images":[{"Description":"Temporal and spatial topography of cell proliferation in cancer","Height":51719,"MaxLevel":6,"Name":"i0","Path":root_url,"Width":36357}],"Layout":{"Grid":[["i0"]]},"Stories":[{"Waypoints":[{"Name":"Workflow Overview","Group":"Cell Type","Overlays":[],"Pan":[0.28475808072588604,0.3478898140133385],"Zoom":4.3956084783177385,"Arrows":[],"Description":"Multiplexed images were generated using tissue-based cyclic immunofluorescence (t-CyCIF) with a \n20X/0.75NA objective.\n\nFollowing acquisition, images are aligned and the fluorescent intensity for each marker was measured. \nThe individual markers are visible in the Channels tab on the right of the screen.\n\nThe following waypoints of this story show how we use groups of markers - found under Channel Groups \non the right of the screen - to assign cell type and to ascribe cells a ternary proliferation state \nand time point in the cell cycle.\n\nYou can change the marker set show by clicking the links in Channels tab on the right side of the screen.\n\nYou will also be able to review the cell typing calls we made, as well as the proliferation and cell \ncycle characterization by toggling the Data Layers masks below the text.\n\nFinally, the data plots within each waypoint are interactive! Click on them to visualize cell type \nclusters or single cell within a scatter plot.\n"},{"Name":"Cell Typing","Group":"Cell Type","Overlays":[],"VisScatterplot":{"config":{"view":{"continuousWidth":400,"continuousHeight":300},"background":null},"data":{"url":root_url+"/visdata/plot1_CD45_eCad.csv"},"mark":{"type":"circle","size":60},"encoding":{"color":{"type":"nominal","field":"Cluster","scale":{"domain":["Immune","Epithelial","Stromal","Other"],"range":["#FFA500","#FFFF33","#9370DB","#A9A9A9"]},"legend":{"direction":"horizontal","orient":"bottom"}},"x":{"type":"quantitative","field":"CD45","scale":{"zero":false},"grid":false},"y":{"type":"quantitative","field":"e-Cadherin","scale":{"zero":false},"grid":false}},"transform":[{"lookup":"clust_ID","from":{"data":{"name":"data-from-yaml"},"key":"clust_ID","fields":["Cluster"]}},{"filter":"(datum.Cluster !== null)"}],"$schema":"https://vega.github.io/schema/vega-lite/v4.8.1.json","datasets":{"data-from-yaml":[{"clust_ID":1,"Cluster":"Tumor"},{"clust_ID":2,"Cluster":"Other"},{"clust_ID":3,"Cluster":"Immune"},{"clust_ID":4,"Cluster":"Stromal"}]},"width":"container"},"VisMatrix":{"config":{"view":{"continuousWidth":400,"continuousHeight":300},"background":null},"data":{"url":root_url+"/visdata/plot2_CellTypeClust.csv"},"mark":"rect","encoding":{"color":{"type":"quantitative","field":"frequency","legend":{"direction":"horizontal","orient":"bottom"}},"x":{"type":"nominal","field":"channel","sort":["CD45","e-Cadherin","Vimentin","aSMA"],"grid":false},"y":{"type":"nominal","field":"type","sort":["Immune","Epithelial","Stromal","Other"],"grid":false}},"$schema":"https://vega.github.io/schema/vega-lite/v4.8.1.json","datasets":{},"width":"container"},"Pan":[0.20932541492,0.35840354865],"Zoom":18.974730173981282,"Arrows":[],"Description":"The tissue environment can be very diverse. Here we see large clusters of immune cells marked with CD45. Stromal cells, marked with Vimentin or a-SMA, are dispersed around the tissue. Epithelial cells represent the majority of the cells in the tissue, and express e-Cadherin on their membrane. Using these markers, we separated the cells into three cell groups: Immune, Stromal, and Epithelial by multidimensional gating. The marker populations for cell typing are distinct; this is illustrated in the scatter plot below which plots CD45 intensity versus e-Cadherin intensity. \n `VisScatterplot`\n The below clustergram shows each group and its expression of the 4 cell-typing markers.\n `VisMatrix`\n The Cell Type Segmentation Masks below show how the cells were ultimately classified.\n We looked into the proliferative state and cell cycle dynamics of the epithelial cell population.","ActiveMasks":null,"Masks":["Cell Type - Immune","Cell Type - Epithelial","Cell Type - Stromal","Cell Type - Other"]},{"Name":"MPI +1: Proliferative","Group":"Proliferation","Overlays":[],"Pan":[0.3302403536980999,0.5389485706601224],"Zoom":15.750259903825325,"Arrows":[{"Angle":60,"HideArrow":false,"Point":[0.3279309627877498,0.5417577551256976],"Text":""},{"Angle":60,"HideArrow":false,"Point":[0.34388988803390064,0.5477897463094479],"Text":""},{"Angle":60,"HideArrow":false,"Point":[0.33465232439250014,0.5358291695050972],"Text":""}],"Description":"With advancements in multiplexed imaging, we have the ability to assess proliferation, arrest, and quiescence with the combination of a host of markers. A Multivariate Proliferation Index (MPI) uses these advances in imaging to assign a proliferation state to each cell.\n\n We found that proliferation markers Ki67, MCM2, and PCNA were present in a graded distribution with MCM2 and PCNA most commonly expressed, although there are many triple positive cells - some   of which are indicated with arrows here.\n These three markers could be looked at together to give a more robust proliferation value.\n The proposed formula for calculating this value is shown below.\n\n ![image] (img/MPI_Eq.png) \n\n Thus, we assign cells that are positive for proliferation markers an MPI of +1.\n ![image](img/MPI_Clust.png) "},{"Name":"MPI +1: Ki67 as a Marker for Proliferation","Group":"Proliferation","Overlays":[],"Pan":[0.3302403536980999,0.5389485706601224],"Zoom":15.750259903825325,"Arrows":[{"Angle":60,"HideArrow":false,"Point":[0.3303003536980999,0.5366003536980999],"Text":""},{"Angle":60,"HideArrow":false,"Point":[0.3462003536980999,0.538003536980999],"Text":""},{"Angle":60,"HideArrow":false,"Point":[0.33520003536980997,0.5326003536980999],"Text":""}],"Description":"Most cells that are scored as proliferative have at least 2 markers of proliferation.   However Ki67 is often absent in cells that have both MCM2 and PCNA (some examples are   shown by arrows).\n\n In our study we found that Ki67 was extremely specific at detecting proliferating MPI+1 cells (specificity ~ 95%). However the sensitivity of Ki67 was low (< 30% in breast samples). This is a prime example of how multiplexing can vastly improve the detection of basic cell biology traits in human tissue samples.\n\n Spoiler alert... This is due to the lower levels of Ki67 in the G1 phase of the cell cycle!\n ![image](img/MPI_Clust.png) "},{"Name":"MPI -1: Arrested","Group":"Arrest","Overlays":[],"Pan":[0.3302403536980999,0.5389485706601224],"Zoom":15.750259903825325,"Arrows":[],"Description":"Similar to proliferation, multiple markers can be used to show arrest. Here we show p21 and p27, two common G1 checkpoint inhibitors.\n If a cell is positive for p21 or p27 it is classified as arrested and given an MPI value of -1.\n\n In cases where a cell is positive for proliferation and arrest markers, the arrest markers take precedence.\n\n Click below to see proliferation and arrest masks.\n ![image](img/MPI_Clust.png) ","ActiveMasks":[],"Masks":["MPI = +1","MPI = -1"]},{"Name":"MPI  0: Non-Proliferative","Group":"Proliferation and Arrest","Overlays":[],"Pan":[0.3302403536980999,0.5389485706601224],"Zoom":15.750259903825325,"Arrows":[],"Description":"Cells with low proliferation markers and low arrest markers are classified as non-proliferative and given an MPI value of 0.\n\n Thus we assign each cell with an MPI value (+1 if proliferative, 0 if non-proliferative and non-arrested, -1 if arrested) and create masks to assess the accuracy of the classifications.\n Click below to view the masks for the three MPI states: proliferative, non-proliferative, and arrested.\n ![image](img/MPI_Clust.png) ","ActiveMasks":null,"Masks":["MPI = +1","MPI = 0","MPI = -1"]},{"Name":"Cell Cycle","Group":"Cell Cycle","Overlays":[],"Pan":[0.5528422646517467,0.4833579409146386],"Zoom":15.750259903825325,"Arrows":[],"VisScatterplot":{"config":{"view":{"continuousWidth":400,"continuousHeight":300},"background":null},"data":{"url":root_url+"/visdata/plot5_CellCycleCMD.csv"},"mark":{"type":"circle","size":60},"encoding":{"color":{"type":"nominal","field":"Cluster","scale":{"domain":["EarlyG1","G1","G1S","S","EarlyG2","G2","LateG2","M"],"range":["#FF0000","#FF9100","#FFEB00","#7ED321","#00B4FF","#002AFF","#BD10E0","#FF33EB"]},"legend":{"direction":"horizontal","orient":"bottom"}},"x":{"type":"quantitative","field":"CMD_X","scale":{"zero":false},"grid":false},"y":{"type":"quantitative","field":"CMD_Y","scale":{"zero":false},"grid":false}},"transform":[{"lookup":"clust_ID","from":{"data":{"name":"data-from-yaml"},"key":"clust_ID","fields":["Cluster"]}},{"filter":"(datum.Cluster !== null)"}],"$schema":"https://vega.github.io/schema/vega-lite/v4.8.1.json","datasets":{"data-from-yaml":[{"clust_ID":1,"Cluster":"EarlyG1"},{"clust_ID":2,"Cluster":"G2"},{"clust_ID":3,"Cluster":"G1S"},{"clust_ID":4,"Cluster":"S"},{"clust_ID":5,"Cluster":"EarlyG2"},{"clust_ID":6,"Cluster":"G2"},{"clust_ID":7,"Cluster":"LateG2"},{"clust_ID":8,"Cluster":"M"}]},"width":"container"},"Description":"With the addition of cell cycle markers, the MPI values can be compared to the phases of the cell cycle. Shown here are a few of the cell cycle markers we stained. G1 is shown with CDT1, S is shown with Cyclin E, G2 is shown with Geminin and pRB, and M phase is shown with pHH3.\n\n We ordered cells from this region by cell-cycle markers and the toric cell-cycle ordering plot is shown below. Clicking on an individual cell within the plot will identify that particular cell in the tissue. `VisScatterplot` \n Click below to see the MPI state masks overlaid on these cells.\n\n Please feel free to explore other known cell cycle markers from the individual markers on the right.","ActiveMasks":[],"Masks":["MPI = +1","MPI = 0","MPI = -1"]},{"Name":"Cell Cycle Coherence","Group":"Cell Cycle","Overlays":[],"Pan":[0.5,0.5],"Zoom":0.5,"Arrows":[{"Text":"1","Angle":60,"HideArrow":true,"Point":[0.5599,0.2108]},{"Text":"2","Angle":60,"HideArrow":true,"Point":[0.5517,0.7605]},{"Text":"3","Angle":60,"HideArrow":true,"Point":[0.2783,0.3452]},{"Text":"4","Angle":60,"HideArrow":true,"Point":[0.1336,0.5417]},{"Text":"8","Angle":60,"HideArrow":true,"Point":[0.1047,0.9111]},{"Text":"9","Angle":60,"HideArrow":true,"Point":[0.1342,0.712]},{"Text":"10","Angle":60,"HideArrow":true,"Point":[0.474,0.5813]},{"Text":"13","Angle":60,"HideArrow":true,"Point":[0.6521,0.9213]},{"Text":"17","Angle":60,"HideArrow":true,"Point":[0.428,0.6283]},{"Text":"18","Angle":60,"HideArrow":true,"Point":[0.3405,0.6458]},{"Text":"19","Angle":60,"HideArrow":true,"Point":[0.163,0.7822]},{"Text":"20","Angle":60,"HideArrow":true,"Point":[0.103,0.7975]},{"Text":"26","Angle":60,"HideArrow":true,"Point":[0.2615,0.8973]},{"Text":"27","Angle":60,"HideArrow":true,"Point":[0.4529,0.336]},{"Text":"28","Angle":60,"HideArrow":true,"Point":[0.5549,0.4587]},{"Text":"29","Angle":60,"HideArrow":true,"Point":[0.5483,0.5674]},{"Text":"30","Angle":60,"HideArrow":true,"Point":[0.6303,0.5619]},{"Text":"31","Angle":60,"HideArrow":true,"Point":[0.1745,0.8587]},{"Text":"32","Angle":60,"HideArrow":true,"Point":[0.2186,0.7096]},{"Text":"33","Angle":60,"HideArrow":true,"Point":[0.3611,0.8047]}],"Description":"Following a pathology review of the tissue, we created an toric cell cycle plot for each distinct ROI and fit a circle to the 2D dimensionally reduced plot. To determine the cell cycle coherence, we looked at two metrics: the angular and radial distribution of the cells around the torus. These two metrics are illustrated in the image below.\n\n ![image](img/Torus_Def.png)\n\n A cell population displays cell cycle coherence when the cell cycle protein markers fluctuate in an organized and coordinated pattern across the populations of cells. When this is the case the points in the reduced dimensionality plot (i.e. the cells' representation) are both 1) spread out and close to outer toric shape and 2) evenly distributed around the contours of the torus (i.e. low distance from the circle fit and low angular variability).\n\n Each ROI's cells arrange themselves into the cell cycle differently, and therefore have different values for theta and d, different distributions of cell cycle markers and visually different tori. ROIs 30, 8, and 10 are shown below to illustrate this.\n \n\n ![image](img/ROI_Tori.png)\n\n Below is a static plot showing all the ROIs' cell cycle coherence metrics.\n\n\n ![image](img/Harmony.png) "}]}]};
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α-SMA","Link":"https://www.genecards.org/cgi-bin/carddisp.pl?gene=ACTA2&keywords=alpha,smooth,muscle,actin"},{"String":"BANF1","Alias":null,"Link":"https://www.genecards.org/cgi-bin/carddisp.pl?gene=BANF1&keywords=BANF1"},{"String":"CD11B","Alias":null,"Link":"https://www.genecards.org/cgi-bin/carddisp.pl?gene=ITGAM&keywords=CD11B"},{"String":"CD14","Alias":null,"Link":"https://www.genecards.org/cgi-bin/carddisp.pl?gene=CD14&keywords=CD14"},{"String":"CD163","Alias":null,"Link":"https://www.genecards.org/cgi-bin/carddisp.pl?gene=CD163&keywords=CD163"},{"String":"CD19","Alias":null,"Link":"https://www.genecards.org/cgi-bin/carddisp.pl?gene=CD19&keywords=CD19"},{"String":"CD20","Alias":null,"Link":"https://www.genecards.org/cgi-bin/carddisp.pl?gene=MS4A1&keywords=CD20"},{"String":"CD21","Alias":null,"Link":"https://www.genecards.org/cgi-bin/carddisp.pl?gene=CR2&keywords=CD21"},{"String":"CD3D","Alias":null,"Link":"https://www.genecards.org/cgi-bin/carddisp.pl?gene=CD3D&keywords=CD3D"},{"String":"CD4","Alias":null,"Link":"https://www.genecards.org/cgi-bin/carddisp.pl?gene=CD4&keywords=CD4"},{"String":"CD45","Alias":null,"Link":"https://www.genecards.org/cgi-bin/carddisp.pl?gene=PTPRC&keywords=CD45"},{"String":"CD45RB","Alias":null,"Link":"https://www.genecards.org/cgi-bin/carddisp.pl?gene=PTPRC&keywords=CD45RB"},{"String":"CD68","Alias":null,"Link":"https://www.genecards.org/cgi-bin/carddisp.pl?gene=CD68&keywords=CD68"},{"String":"CD8A","Alias":null,"Link":"https://www.genecards.org/cgi-bin/carddisp.pl?gene=CD8A&keywords=CD8A"},{"String":"FOXP3","Alias":null,"Link":"https://www.genecards.org/cgi-bin/carddisp.pl?gene=FOXP3&keywords=FOXP3"},{"String":"GFAP","Alias":null,"Link":"https://www.genecards.org/cgi-bin/carddisp.pl?gene=GFAP&keywords=GFAP"},{"String":"GTUBULIN","Alias":"gamma-tubulin","Link":"https://www.genecards.org/cgi-bin/carddisp.pl?gene=TUBG1&keywords=gamma,tubulin"},{"String":"IBA1","Alias":null,"Link":"https://www.genecards.org/cgi-bin/carddisp.pl?gene=AIF1&keywords=IBA1"},{"String":"KERATIN","Alias":"pan-cytokeratin, 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